The key insights of Natural Language Processing (NLP) studies through the use of computational techniques has allowed to apply such field to the resolution of nowadays real-world problems, such as information retrieval or Web-based services, to name a few.
However, the complexity of the human language has led to make separate linguistic analysis to offer performant NLP systems, and one of these is the morphosyntactic analysis of sentences, which is also known as the tagging. In fact, due to the high ambiguity of the human language where one word may have different grammatical values (such as being a verb or a noun), it is of utmost importance to correctly resolve ambiguities in order to avoid misinterpretations. To solve those issues, several approaches have emerged and particularly:                statistical Part Of Speech (POS) Taggers which generally use the so-called Hidden Markov Model (HMM) and the Viterbi algorithm;        formal rule systems;        or a mix of the two.        
The POS systems available on the market work quite well and offering a very high rate of success, for instance above 80% for Written Standard English.
However statistical POS are very dependent on the corpus they use to learn the trigram or bigram frequencies they use, besides they need a human operator tagging by hand a very large corpus to produce sufficient learning set.
Among the POS taggers, the ones using Constraint Grammars are of a particular interest because of their speed, their robustness and their accuracy. Most of the Constraint Grammars based systems intersect a Directed Acyclic Graph (DAG) that represents a text having ambiguity with a Directed Graph (a Finite State Transducer) that represents a set of constraints. However, a major drawback of such implementation is that the graph representation is very complex to deal with, requiring complex algorithms difficult to program and often machine dependent in their implementation. These problems limit the use of such systems to the specific language they are developed for.
Thus, what is needed is a system and method for overcoming the deficiencies of the conventional technology as discussed above.